KR20200032249A - On-device measurement using target decomposition - Google Patents

Abstract

Methods and systems for more efficient X-ray scatterometry measurements of on-device structures are presented herein. X-ray scatterometry measurements of one or more structures over a measurement region include decomposition of one or more structures into a plurality of substructures, decomposition of a measurement region into a plurality of subregions, or both. The disassembled structure, measurement region, or both are simulated independently. Each scattering contribution of the independently simulated degraded structure is combined to simulate the actual scattering of the measured structure within the measurement region. In a further aspect, modeled and measured intensities comprising one or more ancillary structures are utilized to perform measurements of the structures of interest. In another additional aspect, measurement decomposition is utilized to train the measurement model and to optimize measurement recipes for specific measurement applications.

Description

On-device measurement using target decomposition

Cross reference to related applications

This patent application is made by 35 U.S.C. from U.S. Provisional Patent Application Serial No. 62 / 544,911 entitled "Efficient On-Device Metrology Using Target Decomposition", filed August 14, 2017. Priority is claimed under § 119, the subject matter of which is a United States patent provisional application is incorporated herein by reference in its entirety.

Technical field

The described embodiments relate to metrology systems and methods, and in particular to methods and systems for improved measurement of semiconductor structures.

Semiconductor devices, such as logic and memory devices, are typically manufactured by a series of processing steps applied to a specimen. The various features and multiple structural levels of a semiconductor device are formed by these processing steps. For example, lithography, among other things, is one semiconductor manufacturing process that involves creating a pattern on a semiconductor wafer. Additional examples of semiconductor manufacturing processes include, but are not limited to, chemical mechanical polishing, etching, deposition, and ion implantation. Multiple semiconductor devices may be manufactured on a single semiconductor wafer, and then separated into individual semiconductor devices.

The metrology process is used at various stages during the semiconductor manufacturing process to detect defects on the wafer and promote higher yields. To characterize the critical dimensions, film thickness, composition, and other parameters of nanoscale structures, a number of metrology-based technologies are common, including scatterometry implementations, reflectometry implementations, and related analytical algorithms. Is used as The x-ray scatterometry technology offers the potential for high throughput without the risk of sample destruction.

Traditionally, for targets consisting of thin films and / or repeated periodic structures, optical scatterometry critical dimension (SCR) measurements are performed. As devices (eg, logic and memory devices) move toward smaller nanometer-scale dimensions, characterization becomes more difficult. Devices incorporating materials with complex three-dimensional geometries and various physical properties contribute to the difficulty of characterization. For example, modern memory structures are often high aspect ratio three-dimensional structures that make it difficult for optical radiation to penetrate down to the bottom layer. Optical metrology tools utilizing infrared to visible light can penetrate many layers of translucent material, but longer wavelengths that provide good penetration depth do not provide sufficient sensitivity to small deformations. Also, the increasing number of parameters required to characterize a complex structure (eg, FinFET) leads to an increasing parameter correlation. Consequently, the parameters characterizing the target, often, cannot be reliably separated through the available measurements.

In one example, in an attempt to overcome the penetration issue for 3D FLASH devices that utilize polysilicon as one of the stack's alternating materials, longer wavelengths (eg, near infrared) were utilized. However, the mirrored structure of 3D FLASH essentially causes a decrease in light intensity as illumination propagates deeper into the film stack. This causes sensitivity loss and correlation issues in depth. In this scenario, the optical SCD can successfully extract only a reduced set of metrology dimensions with high sensitivity and low correlation.

In another example, opaque high-k materials are increasingly used in modern semiconductor structures. Optical radiation often cannot penetrate the layer composed of these materials. As a result, measurements using thin film scatterometry tools such as optical ellipsometers or reflectometers are becoming increasingly difficult.

In response to these challenges, more complex optical metrology tools have been developed. For example, with multiple illumination angles, shorter illumination wavelengths, larger ranges of illumination wavelengths, and more complete information acquisition from reflected signals (e.g., more traditional reflectivity or ellipsometric signals) ), As well as a number of tools for measuring multiple Mueller matrix elements. However, these approaches allow measurement and measurement applications (e.g., line edge roughness) of many advanced targets (e.g., complex 3D structures, structures smaller than 10 nm, structures utilizing opaque materials). The fundamental challenges associated with (line edge roughness) and line width roughness measurements have not been reliably overcome.

Optical methods may provide non-destructive tracking of process variables between process steps, but regular calibration by destructive methods is required to maintain accuracy in terms of process drift.

Atomic force microscopes (AFM) and scanning-tunneling microscopes (STM) can achieve atomic resolution, but they can only examine the surface of the sample. In addition, AFM and STM microscopes require long scanning times. Scanning electron microscopes (SEM) achieve intermediate resolution levels, but cannot penetrate the structure to a sufficient depth. Therefore, the high aspect ratio hole is not well characterized. In addition, the essential charging of the sample adversely affects imaging performance.

To overcome the penetration depth issue, traditional imaging techniques such as TEM, SEM, etc., use destructive samples such as focused ion beam (FIB) machining, ion milling, blanket or selective etching, etc. It is used in conjunction with preparation skills. For example, transmission electron microscopes (TEM) can achieve high resolution levels and investigate arbitrary depths, but TEM requires destructive cutting of the sample. Multiple iterations of material removal and measurement generally provide the information needed to measure critical metrology parameters across a three-dimensional structure. However, these techniques require sample destruction and long process times. The complexity and time to complete these types of measurements introduces large inaccuracies due to drift in the etch and metrology steps, since the process is completed on the wafer to be measured and the measurement results become available long afterwards. to be. Therefore, the measurement results are subject to bias resulting from further processing and delayed feedback. Moreover, these techniques require numerous iterations that introduce registration errors. In summary, device yield is negatively affected by the long and disruptive sample preparation required for SEM and TEM techniques.

Rather than a simplified throw-away structure fabricated in a scribe line of a semiconductor wafer, an on-device structure or a device-like structure (eg, fabrication in an active area of a semiconductor wafer) It is especially important to perform measurements of the actual functional structure or proxy structure). Measuring the on-device structure eliminates or significantly reduces the bias between the measured structure and the actual device structure, thus increasing the metrological correlation to yield. On-device metrology reduces the area required for larger and specialized metrology targets, potentially increasing the wafer area available for functional devices. However, the on-device structure (eg, a structure located in the active area of the wafer) cannot be damaged by the measurement process. For measurement, the structure cannot be simplified or removed.

The Small-Angle X-Ray Scatterometry (SAXS) system has shown promise for dealing with demanding measurement applications. The SAXS system is capable of non-destructive high-resolution measurements in a relatively large measurement area. However, on-device structures are much more complex than simplified metrology structures, and this complexity poses a significant modeling challenge for SAXS measurements.

It is common to measure and model the largest common unit cell of the structure to be measured. Using this approach, the entire range of periodicity is modeled to compute X-ray scattering. In the case of a complex periodic geometric model, so many features that characterize structures are mathematically explained. In current memory applications, the largest common unit cell may have dimensions on the order of tens of micrometers, while the required measurement resolution is approximately Angstroms. Thus, in practice, modeling the largest common unit cell is very complex, computationally expensive, and error prone. Large and complex models utilized to compute X-ray scattering are computationally expensive, because large periodicity requires the calculation of many scattered orders from many geometric features to accurately estimate actual scattering. .

In summary, the continuous reduction in feature size and the increased depth of many semiconductor structures place difficult requirements on metrology systems. Although SAXS measurement systems have shown promise for dealing with demanding measurement applications, measurement model complexity limits the measurement of parameters of interest in complex periodic structures in a cost-effective and timely manner. Therefore, to maintain high device yields by measuring high aspect ratio structures, improved SAXS metrology systems and methods are desired.

Methods and systems for more efficient X-ray scatterometry measurements of on-device structures are presented herein. Scatterometry targets, such as on-device structures or devices, are necessarily complex to prevent bias between the measurement and the actual device structure. In addition, an area of a semiconductor wafer measured by a metrology system at a given instant or measurement interval may include scatterometry targets such as one or more on-device structures or devices. X-ray scatterometry measurements of scatterometry targets, such as on-devices or devices, are more simply described as an exploded set of sub-structures, measurement sub-area, or both.

In one aspect, X-ray scatterometry measurements of one or more structures across a measurement region include decomposition of one or more structures into a plurality of substructures, decomposition of a measurement region into a plurality of subregions, or both. The disassembled structure, measurement region, or both are simulated independently.

In some embodiments, the complex periodic structure being measured is modeled as the sum of simple periodic substructures.

In some embodiments, the complex periodic structure being measured is modeled as the sum of different periodic shapes of the same periodicity.

In some embodiments, the complex periodic structure being measured is modeled as the sum of different periodic shapes with different periodicity.

In some embodiments, the complex structure being measured is modeled as a sum of relatively simple shapes that are repeated multiple times in an almost periodic fashion.

In some examples, the complex structure being measured is modeled as a simple structure with a small period and another structural perturbation with a large period that is an integer multiple of the small period.

In some embodiments, the measurement region is subdivided into a number of different sub-regions, each associated with a different structure or combination of sub-structures.

In another aspect, the scattering response associated with each resolved measurement element is simulated independently.

In another aspect, each scattering contribution of the independently simulated degraded structure is combined to simulate the actual scattering of the structure measured within the measurement region. The scattering field associated with any combination of the disassembled measurement structure depends on whether the illumination of the underlying structure (s) is coherent, incoherent, or any combination of coherence and incoherence. According to the combination.

In a further aspect, the measured intensity comprising one or more incidental structures and the modeled intensity are utilized to perform measurements of structures of interest. In some examples, on-device measurements are decomposed into important targets for metrology and complex, incidental under-layer structures.

In some instances, measurements are collected from important structures, including contamination by measurement signals from ancillary structures. In addition, measurements are collected from simple structures fabricated on the same incidental underlayer. Measurement decomposition as described herein is utilized to effectively subtract measurement data associated with an important structure by directly subtracting measurement data associated with an important structure from measurement data associated with a simple structure.

In another additional aspect, the ancillary model operates directly on the scattered data measured at the detector and effectively filters the measured data to eliminate the effect of the ancillary structure on the measured data. In some examples, the ancillary model is a heuristic model utilized to account for observed phenomena in measured data known to be associated with the ancillary structure. After filtering the measured data, the resulting filtered measurement data is utilized as part of the model-based measurement of the parameter of interest as described herein.

In another additional aspect, measurement decomposition is utilized to train an input-output measurement model that establishes a functional relationship between the measured scattered intensity and the value of one or more noted parameters.

In another additional aspect, measurement decomposition is utilized to optimize the measurement recipe for a particular measurement application. Optimized measurement recipes include the selection of physical parameters of the measurement system that enhance the signal of interest and suppress the signal from the ancillary structures.

The foregoing is an overview, and thus includes simplification, generalization, and omission of details, as needed; Consequently, those skilled in the art will recognize that the outline is merely illustrative and not limiting in any way. Other aspects, original features, and advantages of the devices and / or processes described herein will become apparent in the non-limiting detailed description described herein.

1 is a diagram illustrating a metrology system 100 configured to perform measurement decomposition according to the methods described herein.
2 depicts an end view of a beam shaping slit mechanism 120 in one configuration.
3 depicts an end view of the beam shaping slit mechanism 120 in another configuration.
4 depicts an x-ray illumination beam 116 incident on the wafer 101 at a specific orientation described by angles φ and θ.
5 is a diagram illustrating a specimen positioning system 140 in which the wafer stage has been moved to a location where the illumination beam 116 is incident on the wafer 101.
6 is a diagram illustrating an example measurement decomposition engine 160 implemented by computing system 130.
7 depicts a unit cell 170 of a semiconductor structure to be measured.
8A depicts a measurement area 179 comprising multiple structures.
8B depicts a plot 180 of a non-uniform distribution of illumination intensity across the measurement area 179.
9A depicts measurement of a composite semiconductor structure 181 using vertical incident illumination 116.
9B depicts an image 185 of scattering intensity associated with measurement of a composite semiconductor structure 181 using normal illumination.
10A depicts a measurement of the same composite semiconductor structure 181 using oblique incident illumination 116.
10B depicts an image 186 of scattering intensity associated with measurement of a composite semiconductor structure 181 using tilted illumination.
11A-11C depict isometric, top, and cross-sectional views, respectively, of a typical 3D FLASH memory device subject to measurement as described herein.
12 depicts another exemplary metrology system 200 configured to perform measurement decomposition according to the methods described herein.
13 depicts a flowchart illustrating an example method 300 for performing model-based X-ray scatterometry measurements as described herein.

Now, examples of background art and detailed references to some embodiments of the invention will be made, examples of some embodiments of the invention being illustrated in the accompanying drawings.

Methods and systems for more efficient X-ray scatterometry measurements of on-device structures are presented herein. Scatterometry targets, such as on-device structures or devices, are necessarily complex to prevent bias between the measurement and the actual device structure. In addition, an area of a semiconductor wafer measured by a metrology system at a given instant or measurement interval may include scatterometry targets such as one or more on-device structures or devices. However, X-ray scatterometry measurements of scatterometry targets, such as on-devices or devices, are more simply described as an exploded set of substructures, measurement subregions, or both.

1 shows an embodiment of a transmission, small-angle x-ray scatterometry (T-SAXS) metrology tool 100 for measuring the properties of a sample according to an exemplary method presented herein. For example. As shown in FIG. 1, system 100 may be used to perform T-SAXS measurements across measurement area 102 of sample 101 illuminated by an illumination beam spot.

In the depicted embodiment, metrology tool 100 includes an x-ray illumination source 110 that is configured to generate x-ray radiation suitable for T-SAXS measurements. In some embodiments, the x-ray illumination source 110 is configured to generate wavelengths between 0.01 nanometers and 1 nanometer. In general, to provide x-ray illumination for T-SAXS measurements, any suitable high-brightness x-ray illumination source capable of generating high-brightness x-rays at a flux level sufficient to enable high-throughput inline measurements will be considered. It might be. In some embodiments, the x-ray source includes a tunable monochromator that enables the x-ray source to deliver x-ray radiation at different selectable wavelengths.

In some embodiments, one or more x-rays emitting radiation having a photon energy greater than 15 keV to ensure that the x-ray source supplies light at a wavelength that allows sufficient transmission through the wafer substrate as well as the entire device. Source is utilized. By way of non-limiting example, particle accelerator source, liquid anode source, rotating anode source, stationary, solid anode source, microfocus source, microfocus source Any of a microfocus rotating anode source, a plasma-based source, and an inverse Compton source may be utilized as the x-ray illumination source 110. In one example, a reverse Compton source available from Lyncean Technologies, Inc. of Palo Alto, California, USA may be considered. The inverse Compton source has the additional advantage that it can generate x-rays over a range of photon energies, thereby enabling the x-ray source to deliver x-ray radiation at different selectable wavelengths.

Exemplary x-ray sources include electron beam sources that are configured to impact a solid or liquid target to stimulate x-ray radiation. Methods and systems for generating high brightness, liquid metal x-ray illumination are described in US Pat. No. 7,929,667 issued April 19, 2011 to KLA-Tencor Corp., the entire contents of which are hereby incorporated by reference. Is incorporated into.

The X-ray illumination source 110 produces x-ray emission across a source region having a finite lateral dimension (ie, a non-zero dimension orthogonal to the beam axis). The focusing optics 111 focus the source radiation onto a metrology target located on the sample 101. The finite lateral source dimension is represented by the finite spot size 102 on the target defined by the ray 117 originating from the edge of the source. In some embodiments, focusing optics 111 includes an elliptical shaped focusing optical element.

The beam divergence control slit 112 is positioned in the beam path between the focusing optics 111 and the beam shaping slit mechanism 120. The beam divergence control slit 112 limits the divergence of illumination provided to the sample to be measured. The additional intermediate slit 113 is located in the beam path between the beam divergence control slit 112 and the beam shaping slit mechanism 120. The intermediate slits 113 provide additional beam shaping. However, in general, the intermediate slits 113 are optional.

The beam forming slit mechanism 120 is located just before the sample 101 in the beam path. In one aspect, the slit of the beam shaping slit mechanism 120 is positioned close to the sample 101 to minimize magnification of the incident beam spot size due to beam divergence defined by a finite source size. In one example, the expansion of the beam spot size due to the shadow generated by the finite source size is approximately 1 for a 10 micrometer x-ray source size and a distance of 25 millimeters between the beam forming slit and sample 101. Micrometer.

In some embodiments, the beam shaping slit mechanism 120 includes a number of independently operated beam shaping slits (ie, blades). In one embodiment, the beam shaping slit mechanism 120 includes four independently operated beam shaping slits. These four beam forming slits effectively block a portion of the incoming beam 115 and produce an illumination beam 116 having a box-shaped illumination cross-section.

2 and 3 depict end views of the beam shaping slit mechanism 120 depicted in FIG. 1 in two different configurations. As illustrated in Figures 2 and 3, the beam axis is perpendicular to the drawing page. As depicted in Figure 2, the incoming beam 115 has a large cross section. In some embodiments, incoming beam 115 has a diameter of approximately 1 millimeter. Moreover, the position of the beam 115 entering in the beam forming slits 126-129 may have an uncertainty of approximately 3 millimeters due to the beam pointing error. To accommodate the uncertainty of the incoming beam size and beam position, each slit has a length L of approximately 6 millimeters. As depicted in Figure 2, each slit is movable in a direction perpendicular to the beam axis. In the example of FIG. 2, slits 126-129 are positioned at a maximum distance from the beam axis, ie, the slits are fully open and they do not restrict light passing through the beam forming slit mechanism 120.

3 is a beam shaping slit in a position to block a portion of the incoming beam 115, such that the outgoing beam 116 delivered to the sample to be measured has a reduced size and well-defined shape. The slits 126-129 of the mechanism 120 are depicted. As depicted in FIG. 3, each of the slits 126-129 moved inward toward the beam axis to achieve the desired output beam shape.

The slits 126-129 are made of a material that minimizes scattering and effectively blocks incident radiation. Exemplary materials include single crystal materials such as germanium, gallium arsenide, indium phosphide, and the like. Typically, the slit material is cleaved along the crystallographic direction, rather than sawn, to minimize scattering across structural boundaries. Further, the slit is oriented with respect to the incoming beam, such that the interaction between incoming radiation and the internal structure of the slit material produces a minimal amount of scattering. Crystals are attached to each slit holder made of a high density material (eg tungsten), for complete blocking of the x-ray beam on one side of the slit. In some embodiments, each slit has a rectangular cross section having a width of approximately 0.5 millimeters and a height of approximately 1-2 millimeters. As depicted in Figure 2, the length L of the slit is approximately 6 millimeters.

In general, x-ray optics shape x-ray radiation and direct it to the sample 101. In some examples, the x-ray optics include an x-ray monochromator that monochromatizes the x-ray beam incident on the sample 101. In some examples, the x-ray optics collimate or focus the x-ray beam onto the measurement area 102 of the sample 101 using a multi-layer x-ray optics with a divergence of less than 1 milliradian. In these examples, multilayer x-ray optics also function as a beam monochromator. In some embodiments, the x-ray optics include one or more x-ray collimating mirrors, x-ray apertures, x-ray beam stops, refractive x-rays Optics, diffractive optics such as zone plates, Montel optics, specular x-ray optics such as grazing incidence ellipsoidal mirrors, hollow capillary x-ray waveguides polycapillary optics, such as hollow capillary x-ray waveguides, multilayer optics or systems, or any combination thereof. Additional details are described in US Patent Publication No. 2015/0110249, the contents of which are incorporated herein by reference in their entirety.

The x-ray detector 119 collects the x-ray radiation 114 scattered from the sample 101, and the properties of the sample 101 sensitive to incident x-ray radiation according to the T-SAXS measurement modality The output signal 135 is generated. In some embodiments, scattered x-rays 114 are collected by x-ray detector 119 while sample positioning system 140 positions sample 101 to produce an angled scattered x-ray. Orient.

In some embodiments, the T-SAXS system includes one or more photon counting detectors with a high dynamic range (eg, greater than 10 ⁵ ). In some embodiments, a single photon counting detector detects the location and number of photons detected.

In some embodiments, the x-ray detector decomposes one or more x-ray photon energy and generates a signal for each x-ray energy component representing the properties of the sample. In some embodiments, the x-ray detector 119 is a CCD array, microchannel plate, photodiode array, microstrip proportional counter, gas filled proportional counter, scintillator, or fluorescence. Any of the materials.

In this way, X-ray photon interactions within the detector are distinguished by energy in addition to the number of pixel positions and counts. In some embodiments, X-ray photon interaction is distinguished by comparing the energy of the X-ray photon interaction to a predetermined upper threshold and a lower predetermined threshold. In one embodiment, this information is communicated to computing system 130 via output signal 135 for further processing and storage.

Each orientation of the illuminated x-ray beam 116 relative to the surface normal of the semiconductor wafer 101 is described by any two angular rotations of the wafer 101 relative to the x-ray illuminated beam 115, or The opposite is also possible. In one example, the orientation can be described with respect to a coordinate system fixed to the wafer. 4 depicts an x-ray illumination beam 116 incident on the wafer 101 at a specific orientation described by the incident angle θ and the azimuth angle φ. The coordinate frame XYZ is fixed to the measurement system (e.g., illumination beam 116) and the coordinate frame X'Y'Z 'is fixed to the wafer 101. The Y axis is aligned with the surface of the wafer 101 in a plane. X and Z are not aligned with the surface of the wafer 101. Z 'is aligned with an axis perpendicular to the surface of the wafer 101, and X' and Y 'are in a plane aligned with the surface of the wafer 101. As depicted in Figure 4, the x-ray illumination beam 116 is aligned with the Z axis and thus lies within the XZ plane. The angle of incidence θ describes the orientation of the x-ray illumination beam 116 with respect to the surface normal of the wafer in the XZ plane. Moreover, the azimuth angle phi describes the orientation of the XZ plane with respect to the X'Z 'plane. In summary, θ and φ uniquely define the orientation of the x-ray illumination beam 116 with respect to the surface of the wafer 101. In this example, the orientation of the x-ray illumination beam with respect to the surface of the wafer 101 is rotated about an axis perpendicular to the surface of the wafer 101 (ie, the Z 'axis) and the surface of the wafer 101. This is explained by rotation about the axis being aligned (ie, the Y axis). In some other examples, the orientation of the x-ray illumination beam with respect to the surface of the wafer 101 is different from the first axis aligned with the surface of the wafer 101 and perpendicular to the first axis aligned with the surface of the wafer 101. It is explained by rotation about an axis.

As illustrated in FIG. 1, metrology tool 100 allows both alignment of sample 101 and orientation of sample 101 over a large range of angles of incidence and azimuth with respect to illumination beam 116. And a sample positioning system 140 configured. In some embodiments, the sample positioning system 140 is configured to rotate the sample 101 over a large range of rotational angles (eg, at least 60 degrees) aligned within a plane with the surface of the sample 101. do. In this way, angularly resolved measurements of sample 101 are collected by metrology system 100 over any number of positions and orientations on the surface of sample 101. In one example, the computing system 130 sends a command signal (not shown) to the sample positioning system 140 indicating the desired location of the sample 101. In response, the sample positioning system 140 generates command signals to various actuators of the sample positioning system 140 to achieve the desired positioning of the sample 101.

5 depicts a sample positioning system 140 in one embodiment. As depicted in FIG. 5, the sample positioning system 140 includes a base frame 141, a transverse alignment stage 142, a stage reference frame 143, and a wafer stage 144. For reference purposes, the {X _BF , Y _BF , Z _BF } coordinate frame is attached to the base frame 141, and the {X _NF , Y _NF , Z _NF } coordinate frame is attached to the lateral alignment stage 142. , {X _RF , Y _RF , Z _RF } coordinate frames are attached to the stage reference frame 143, and {X _SF , Y _SF , Z _SF } coordinate frames are attached to the wafer stage 144. Wafer 101 is supported on wafer stage 144 by a tip-tilt-Z stage 156 that includes actuators 150A-C. The rotating stage 158 mounted on the tip tilt Z stage 156 orients the wafer 101 with respect to the illumination beam 116 over a range of azimuth angles φ. In the depicted embodiment, three linear actuators 150A-C are mounted to the wafer stage 144 and support the rotating stage 158, which then supports the wafer 101.

The actuator 145 translates the transverse alignment stage 142 relative to the base frame 141 along the X _BF axis. The rotary actuator 146 rotates the stage reference frame 143 relative to the lateral alignment stage 142 about a rotation axis 153 aligned with the Y _NF axis. The rotating actuator 146 orients the wafer 101 with respect to the illumination beam 116 over a range of incident angles θ. Wafer stage actuators 147 and 148 translate wafer stage 144 relative to stage reference frame 143 along the X _RF and Y _RF axes, respectively. The actuators 150A-C, in cooperation, translate the rotating stage 158 and the wafer 101 relative to the wafer stage 144 in the Z _SF direction, and the wafer stage around an axis coplanar with the X _SF -Y _SF plane. Tilt and tilt the rotating stage 158 and the wafer 101 with respect to 144. The rotation stage 158 rotates the wafer 101 around an axis perpendicular to the surface of the wafer 101.

In summary, the wafer stage 144 allows the illumination beam 116 to enter any location on the surface of the wafer 101 (ie, at least 300 millimeters in the X _RF and Y _RF directions). ), The wafer 101 can be moved. Rotating actuator 146 is relative to illumination beam 116 such that illumination beam 116 may be incident on the surface of wafer 101 at any of a large range of angles of incidence (eg, greater than 2 degrees). The stage reference frame 143 may be rotated. In one embodiment, the rotary actuator 146 is configured to rotate the stage reference frame 143 over a range of at least 60 degrees. A rotating actuator 158 mounted on the wafer stage 144 may cause the illumination beam 116 to enter the surface of the wafer 101 at any of a large range of azimuthal angles (eg, at least a 90 degree rotation range). Thus, the wafer 101 can be rotated relative to the illumination beam 116.

In some other embodiments, lateral alignment stage 142 is removed and stage reference frame 143 is rotated relative to base frame 141 by rotating actuator 146. In these embodiments, the x-ray illumination system provides one or more optical elements of the x-ray illumination system that cause the x-ray illumination beam 116 to move relative to the base frame 141, eg, in the X _BF direction. And one or more actuators to move. In these embodiments, the movement of the stage reference stage 143 is, for example, the movement of one or more optical elements of the x-ray illumination system to move the x-ray illumination beam to a desired position relative to the rotation axis 153. Is replaced.

In the depicted embodiment, the beam shaping slit mechanism 120 is configured to rotate about the beam axis in cooperation with the orientation of the sample to optimize the profile of the incident beam for each angle of incidence, azimuth, or both. In this way, the beam shape is matched to the shape of the metrology target. As depicted in FIG. 5, the rotating actuator 122 rotates the frame 120 and all attached mechanisms, actuators, sensors, and slits around the axis 116 of the illumination beam.

In a further aspect, the T-SAXS system is utilized to determine a sample's properties (eg, structural parameter values) based on one or more diffraction orders of scattered light. As depicted in FIG. 1, the system 100 acquires the signal 135 generated by the detector 119 and determines the properties of the sample based at least in part on the obtained signal and determines the parameter of interest 139 determined It includes a computing system 130 that is utilized to store in a memory (eg, memory 190).

In some embodiments, SAXS-based metrology involves sizing a sample by inverse solution of a predetermined measurement model with measured data. The measurement model includes several (approximately ten) adjustable parameters and represents the geometry and optical properties of the sample and the optical properties of the measurement system. Inverse methods include, but are not limited to, model-based regression, tomography, machine learning, or any combination thereof. In this way, the target profile parameter is estimated by decomposing the value of a parameterized measurement model that minimizes the error between the measured scattered x-ray intensity and the modeled result.

In some embodiments, the measurement model is an electromagnetic model of measurement (eg, a Born Wave Model) that produces an image representative of scattering from the target to be measured. For example, the images 185 and 186 depicted in FIGS. 9B and 10B are images showing scattering from a target to be measured. The modeled image may be parameterized by process control parameters (eg, etch time, etch slope, etch selectivity, deposition rate, focus, dosage, etc.). The modeled image is also a structural parameter of the measured structure (eg, height, diameter at different heights, alignment of holes relative to other structures, straightness of hole features, concentricity of hole features, deposition as a function of depth. Layer thickness, uniformity of deposited layers over specific hole features or between different hole features, etc.).

The measured scattering image is utilized to estimate the value of one or more of the parameters of interest by performing an inverse solution. In some examples, the inverse estimates values of process parameters, geometric parameters, or both, that produce a modeled scattering image that best matches the measured image. In some examples, the space of the scattering image is retrieved using a measurement model by a regression method (eg, gradient descent, etc.). In some examples, a library of pre-computed images is created, and the library is searched for values of one or more of the parameters of interest that appear to be the best match between the modeled and measured images.

In some other examples, the measurement model is trained by machine learning algorithms to correlate many samples of scattered images and known process conditions, geometric parameter values, or both. In this way, the trained measurement model maps the measured scattering image to estimated values of process parameters, geometric parameters, or both. In some examples, the trained measurement model is a signal response metrology (SRM) model that defines a direct functional relationship between the actual measurement and the parameter of interest.

In general, any of the trained models described herein are implemented as neural network models. In other examples, any of the trained models may include a linear model, a nonlinear model, a polynomial model, a response surface model, a support vector machines model, a decision tree model, It may be implemented as a random forest model, a deep network model, a convolutional network model, or other type of model.

In some examples, any of the trained models described herein may be implemented as a combination of models. An additional description of model training and the use of trained measurement models for semiconductor measurements are provided in US Patent Publication No. 2016/0109230 by Pandev et al., The contents of which are incorporated herein by reference in their entirety. do.

In some other examples, a free-form model that does not include pre-considered geometric shapes and material distributions describes the geometric shapes and material parameters of the structure to be measured. In some examples, the model includes many small voxels (volume elements), each with independently adjustable material parameter values (eg, electron density, absorption, or complex refractive index). In some other embodiments, the material properties are piecewise constant. The attributes associated with each different material are determined a priori. The boundary between different materials is a free-form surface, which can be determined by a leveling algorithm.

The measured scatterometry data is used to calculate the image of the sample. In some examples, the image is a two dimensional (2-D) map of electron density, absorption, complex refractive index, or a combination of these material properties. In some examples, the image is a three dimensional (3-D) map of electron density, absorption, complex refractive index, or a combination of these material properties. Maps are created using relatively few physical constraints. These techniques are described in more detail in US Patent Publication No. 2015/0300965 by Sezginer et al., The subject matter of which is incorporated herein by reference in its entirety.

In some embodiments, it is desirable to perform measurements at a large range of angles of incidence and azimuth to increase the precision and accuracy of the measured parameter values. This approach reduces the correlation between parameters by expanding the number and variety of data sets available for analysis to include various large angles out of the plane orientation. For example, in normal orientation, T-SAXS can decompose the critical dimension of a feature, but is not very sensitive to the sidewall angle and height of the feature. However, by collecting measurement data over a wide range of out-of-plane angular orientation, the sidewall angle and height of the feature can be resolved. In another example, measurements performed at a large range of angles of incidence and azimuth provide sufficient resolution and penetration depth to characterize high aspect ratio structures through their entire depth.

A measure of the intensity of the diffracted radiation is collected as a function of the x-ray angle of incidence relative to the wafer surface normal. The information included in multiple diffraction orders is typically unique between each model parameter under consideration. Thus, x-ray scattering yields estimation results for the values of the parameters of interest with small errors and reduced parameter correlation.

In one aspect, X-ray scatterometry measurements of one or more structures across a measurement region include decomposition of one or more structures into a plurality of substructures, decomposition of a measurement region into a plurality of subregions, or both. The measurement area is the area of the semiconductor wafer that is measured by the metrology system at a given instant or measurement interval (eg, the duration of data collection for individual measurements). The disassembled structure, measurement region, or both are simulated independently. For X-ray scatterometry measurement applications that are accurately represented by Born Approximation, a sufficiently accurate metrology model is generated based on the independent properties of substructures, subregions, or both. Due to the weak scattering of X-Ray, SAXS measurements of semiconductor structures generally follow the Born approximation method.

For X-ray scatterometry measurements of periodic structures accurately represented by the Born approximation, the field strength for a given scattering order from an infinitesimal slice of the structure at a given height of the structure is linear to the Fourier coefficient of the periodic structure. Is proportional to the enemy. Total field strength requires the integration of all strengths in the vertical direction. Since integration is a linear operation, fields from any layer accumulate linearly. Similarly, for X-ray scatterometry measurements of nearly periodic structures that are accurately represented by the Born approximation, the field strength for a given scattering order is linearly proportional to the approximation of the Fourier coefficient of the nearly periodic structure in the same form as the pure periodic structure. do. Thus, there is a linear relationship between scattering and certain periodic or near periodic structures. For example, scattering of two periodic structures stacked on top of each other is a linear combination of scattering from each individual periodic structure.

6 is a diagram illustrating an example measurement decomposition engine 160 implemented by computing system 130. As depicted in FIG. 6, the measurement decomposition engine 160 constructs a structural decomposition module that generates a structural model associated with each of the plurality of decomposed measurement elements S ₁ ,…, S _N. decomposition module) 161, where N is any suitable integer value. In some examples, the disassembled measurement element is a substructure of the structure to be measured. In some other examples, the disassembled measurement element includes any structure (s) or substructure (s) that are measured within a subregion of the measurement region. In some embodiments, one or more of the structure models (ie, 162 ₁ ,…, 162 _N ), measured substructure (s), substructure (s) or substructure (s) in subregion (s), or both Also includes material properties associated with. Each structure model (162 ₁ , ..., 162 _N ) is passed to the corresponding response module (163 ₁ , ..., 163 _N ). Each response module 163 ₁ ,…, 163 _N independently generates scattering responses 164 ₁ ,…, 164 _N corresponding to each decomposed measurement element S ₁ ,…, S _N.

In some embodiments, the complex periodic structure being measured is modeled as the sum of simple periodic substructures. In these embodiments, structure decomposition module 161 generates a structure model associated with each of the simple periodic substructures. The scattering associated with each of these substructures is simulated independently. In the case of a structure having a complex periodic geometric shape, in order to approximate the complex structure, various simple periodic shapes are fitted to each other. In this way, complex structures are mathematically approximated by the summation of several simple periodic shapes. In this way, modeled X-ray scattering is essentially the same as scattering of complex structures. In some instances, to approximate complex structures, different periodic shapes of the same periodicity are utilized. In some examples, to approximate complex structures, different periodic shapes with different periodicity are utilized. In some examples, to approximate a complex structure, a relatively simple shape that is repeated many times in an almost periodic fashion is utilized.

In some instances, complex shapes consume very little of the total volume of the periodic model, including simple shapes that are periodically replicated. In some of these examples, complex periodic structures are approximated as simple structures with small periods and other structural disturbances with large periods that are integer multiples of small periods. In this way, scattered orders of small and large periods that overlap (ie share the same scattering angle as the measurement in Q-space) are summed.

7 depicts a unit cell 170 of a semiconductor structure to be measured. Each unit cell 170 includes an array of contact structures 171 fabricated on embedded line structures 172. The buried line structure 172 includes lines 173 of material that are periodically interrupted by blocks 174 of different materials. The spatial periodicity of the buried line structure 172 is significantly greater than that of the contact 171. In one example, every tenth contact corresponds to a block 174 of material. In this example, every tenth scattered order of the array of contacts 171 overlaps each scattered order of each block of different material (ie, shares the same scattering angle as the measurement in Q space). . In this example, the metrology structure is a series of repeating unit cells 170. In this example, only the unit cell 170 is modeled, not the entire metrology structure. Moreover, the scattering associated with each different sub-structure (ie, an array of buried line structures 172 and contact structures 171) is summed in Q space. In this way, scattering associated with each different substructure is simulated independently and summed to reach an estimate of the scattering of the metrology structure.

The measurement region may include a scatterometry target, such as a number of on-device structures or devices. In some embodiments, the measurement region is subdivided into a number of different sub-regions, each associated with a different structure or combination of sub-structures. In these embodiments, the structure decomposition module 161 generates a structure model associated with each substructure of each subregion or each of the subregions. The scattering associated with each of these sub-regions is simulated independently.

8A depicts measurement region 179 including structure 178 and portions of structures 176 and 177. In some examples, the illumination intensity is uniform across the measurement area 179. In these examples, the intensity contribution from each region is proportional to the area of each sub-region. For example, the subregion associated with the background of the measurement region 179 is 20% of the measurement region, the subregion associated with the structure 176 is 60% of the measurement region 179, and is associated with the structure 177 The sub-region to be 10% of the measurement area 179, and the sub-region associated with the structure 178 is 10% of the measurement area 179. However, in some other examples, the illumination intensity is not uniform across the measurement area 179. For example, FIG. 8B depicts a plot 180 of the illumination intensity of a non-uniform distribution across the measurement area 179. In these examples, the intensity contribution from each area is calculated by integrating the intensity distribution of each different sub-region of the measurement area to determine the intensity contribution from each area.

In another aspect, the scattering response associated with each resolved measurement element is simulated independently. As depicted in FIG. 6, as a non-limiting example, each response module 163 ₁ ,…, 163 _N is independent of the scattering response 164 ₁ ,…, 164 _N corresponding to each decomposed measurement element. Is created by Generally, the complex scattered field associated with each decomposed structure is calculated independently. In general, any suitable electromagnetic modeling solver (e.g., Finite Element Method (FEM), Rigorous Coupled Wave Analysis (RCWA), Born Analysis, etc.) To simulate the scattered field associated with each decomposed measurement element using the Fourier transform of each decomposed measurement element is calculated and used. In a preferred embodiment, each resulting scattering field is propagated through the system model to reach an estimate of the scattering field associated with each resolution measurement element at the detector. In some other embodiments, the scattering field associated with each decomposed measurement element is combined at the target and the combined scattering field is propagated through the system model to reach an estimate of the combined scattering field at the detector.

In another aspect, each scattering contribution of the independently simulated degraded structure is combined to simulate the actual scattering of the structure measured within the measurement region. As depicted in FIG. 6, as a non-limiting example, the signal recombination module 165 has a modeled intensity 166 for the combination of scattering responses corresponding to each degraded measurement element at the detector. Estimate.

In general, the scattering field associated with any combination of decomposed measurement structures depends on whether the illumination of the underlying structure (s) is coherent, incoherent, or any combination of coherence and incoherence. According to the combination. In other words, if the path of all interfering waves from the disassembled measurement structure differs more than the coherence length of the illumination source, the illumination is perfectly incoherent. If the path of all interfering waves from the disassembled measurement structure differs less than the coherent length of the illumination source, the illumination is completely coherent. If the path of some interfering waves from the disassembled measurement structure differs less than the interference length of the illumination source, and some interfering waves from the disassembled measurement structure differ more than the coherence length of the illumination source, the illumination Is a combination of coherent and non-coherent.

As an example, the scattering amplitude of the scattering field associated with the disassembled measuring structure S ₁ is given by A ₁ . Similarly, the scattering amplitude of the scattering field associated with the decomposed measuring structure S ₂ is given by A ₂ .

If the illumination of the disassembled measurement structures S ₁ and S ₂ is considered to be coherent, the combined intensity at the same point in q space is a complex conjugate of the sum of the scattering amplitudes, as illustrated by equation (1). It is calculated as the sum of the scattering amplitudes multiplied by.

If the illumination of the disassembled measurement structures S ₁ and S ₂ is considered incoherent, then the combined intensity at the same point in the q space is multiplied by its complex conjugate, as illustrated by equation (2) Is calculated as the sum of each scattering amplitude, i.e., the intensity associated with the scattering field of each decomposed measurement structure at the detector.

In the case of an ideal detector, illumination, and target, all photons reaching each point on the detector correspond to a unique point in the q space, i.e. a unique scattering angle from the target. However, in practice, various non-idealities such as finite spot size on the target, non-zero divergence of the beam, aperiodic at the target, etc., are finite point diffusion functions at each scattering angle. (finite point spread function). Due to these non-ideal properties, it is common, for example, that some of the photons received at a point on the detector come from two different orders. q Since light is scattered at two different points in space, the intensity is added incoherently.

If the illumination of the disassembled measurement structures S ₁ and S ₂ is considered to be both incoherent and incoherent, the combined intensity at the detector is the combined intensity as estimated by equation (1) and It is calculated as the combination of combined strengths as measured by equation (2). For example, if the illumination of the disassembled measurement structures S ₁ and S ₂ is considered to be semi-incoherent and semi-coherent, the combined intensity may be estimated as (0.5 * I _coherent + 0.5 * I _incoherent ). It might be. In this way, the mixture of decomposed measuring structures S ₁ and S ₂ is considered as a linear combination of coherent and non-coherent scatterers.

In general, the decomposition of one or more measured structures allows for simplified simulation. Discretization, transformation calculations, and electromagnetic simulations are performed independently for each decomposed structure using dramatically less computational effort than the same calculations performed for complex models of unit cells of the entire period.

In general, complex combinations of structures such as on-device structures or devices may be measured by decomposition. Direct modeling of such complex combinations of structures, otherwise, would be enormously expensive in time and computing resources.

However, the amount of collected signal for on-device measurement can be large. Thus, storing these signals and functions of these signals (eg, Jacobians and Hessians) can require significant resources. In a further aspect, principal component analysis or any other suitable data compression methodology (eg, linear or nonlinear compression) is utilized to reduce the dimensions of the collected signals and associated transformations.

In a further aspect, measurement decomposition is utilized as part of measurement model simulation, measurement model training, or measurement recipe development.

In some embodiments, the measured overlay metrology target comprises two different structures in different sub-areas of the measurement region. In one example, half of the metrology target is a grating oriented in one direction (eg, x direction), while the other half of the metrology target is a grating oriented in an orthogonal direction (y direction). In these embodiments, overlays in both directions (eg, x and y directions) are measured simultaneously by measurement decomposition (ie, a linear combination of signals measured from each grating structure).

In some embodiments, the measured overlay metrology target includes a combination of structures that are designed to measure overlay between two or more layers simultaneously. In these metrology targets, different parts of the target represent overlays between different layers (e.g., overlays between three back end of the line layers M1, V0, M0).

SAXS systems often illuminate unintended areas of the device. For example, the R-SAXS system illuminates a large area and the T-SAXS system illuminates a buried structure. Therefore, it is common for incidental non-essential data to be measured on the detector.

9A depicts measurement of a composite semiconductor structure 181 using vertical incident illumination 116. The composite semiconductor structure includes an array of holes 182 fabricated over a buried line structure 183 that includes lines of material that are periodically blocked by blocks of different materials 184. 9B depicts an image 185 of scattering intensity associated with T-SAXS measurements of a composite semiconductor structure 181 using vertical illumination. As depicted in Figure 9B, only scattering from etched holes is observed.

10A depicts a measurement of the same composite semiconductor structure 181 using oblique incident illumination 116. 10B depicts an image 186 of scattering intensity associated with T-SAXS measurements of a composite semiconductor structure 181 using tilted illumination. As depicted in FIG. 10B, scattering from both etched holes and buried lines 183 is observed.

In a further aspect, measured strength and modeled strength comprising one or more ancillary structures are utilized to perform measurements of the structures of interest. In some instances, on-device measurements are broken down into complex underlayer structures that serve as important targets and ancillary structures for metrology. The ancillary structure scatters the illumination light detected at the detector, but the ancillary structure is not of interest. Thus, scatterometry measurements of critical structures are contaminated with measurement signals from secondary structures.

In some instances, measurements are collected from important structures, including contamination by measurement signals from ancillary structures. In addition, measurements are collected from simple structures fabricated on the same incidental underlayer. The measurement decomposition as described herein is utilized to effectively offset the measurement signal associated with the ancillary substratum by directly subtracting the measurement data associated with the important structure from the measurement data associated with the simple structure.

In another additional aspect, measurements of multiple structures, each having a different combination of degraded substructures, are performed. In some embodiments, scattering from ancillary structures is modeled based on measurements of multiple structures. In some embodiments, scattering from ancillary structures is modeled based on measurements of multiple structures in which one of the structures does not include ancillary structures.

As illustrated in FIG. 10B, measurement of the structure 181 using tilted illumination is scattered from the final patterned structure (ie, the array of holes 182) and the lower layer structure (ie, the buried line 183). Produces However, in some embodiments, in order to measure the final patterned structure, it is not necessary to build a detailed parametric model of the lower layer structure.

In another additional aspect, the model of the SAXS measurement system includes an arbitrary sub-model of the underlayer structure (eg, a random model), the model decomposition to decompose the measurement and separate the signal associated with the final patterned structure. It is utilized for. The periodic structure is scattered at a certain angle in Q space. However, random structures are scattered at many different angles in the Q space. Thus, measurement decomposition is utilized to separate scattering associated with one or more measured periodic structures and random scatterers (eg, underlayer structures).

In some examples, the interaction of the diffraction orders is decomposed based on a model of the structure of interest that is parameterized by one or more parameters of interest (eg, critical dimension, overlay, etc.), and the underlying ancillary structure is arbitrary It is modeled by a parametric model (eg, a random model). Any parametric model is limited by the system model.

In one example, the measurement model regresses the parameter of interest intended in the presence of an ancillary structure (eg, a random underlying structure).

In another example, the measurement model regressively estimates the random parametric model to identify ancillary data. The ancillary data is excluded from the measured data to regenerate equivalent ancillary free data from the combined measured data. The measurement model then regressively estimates the intended parameter of interest from the ancillary free data.

In another additional aspect, the ancillary model operates directly on the scattered data measured on the detector and effectively filters the measured data to remove the effect of the ancillary structure on the measured data. In some examples, the ancillary model is a heuristic model utilized to account for observed phenomena in measured data known to be associated with the ancillary structure. The ancillary model may be a linear model with constant coefficients operating on a set of basic functions. The coefficient is adjusted to remove as much ancillary data as possible from the measured data. After filtering the measured data, the resulting filtered measurement data is utilized as part of the model-based measurement of the parameter of interest as described herein.

In some other examples, the observed scattering functions are extracted through the model of the SAXS system by training deconvolution, model fitting, regression models (e.g., neural network models, etc.), and measurements associated with structures of interest. Isolate the signal. This approach is especially useful for measuring on-device logic structures. In these measurement applications, structures often include spaces interrupted by periodic and aperiodic line cuts. X-ray scattering from the randomized cuts can be compensated by adjusting the model of the SAXS system, for example, by flux re-normalization. For example, flux normalization mitigates reduced scattering due to random cuts that do not contribute to the first order diffraction peaks. In addition, the size of the line cut, as well as the value of the parameter of interest for the periodic target, may be determined based on measurement decomposition as described above herein.

In another additional aspect, a signal response metrology model (e.g., neural network model, deep learning network model, support vector machine model, etc.) that establishes a functional relationship between the measured scattered intensity and the value of one or more noted parameters. To train an input-output measurement model such as, measurement decomposition is utilized.

In some examples, relatively simple scatterometry structures and more complex structures such as on-devices or devices, to train a library / model capable of estimating the value of a parameter of interest from measurement of a structure such as on-device or device Measurement data is collected from. In some instances, data collected from relatively simple scatterometry targets are separated from data collected by regions of larger structures by measurement decomposition. In some examples, selectable illumination systems (eg, for controlling illumination, acoustooptic modulators, digital mirror devices, selectable apertures, etc.) can measure the size of the measurement area (eg, from 5 micrometers). Changes (over 1 millimeter) allow for relatively simple scatterometry structures and separate illumination of structures such as more complex on-devices or devices.

A relatively simple scatterometry target can be one or more easily characterized regions of a larger structure or a physically separated structure. By measurement decomposition, selectable illumination, or both, damage-free measurement data enables accurate measurement model training. The trained measurement model enables faster measurement of composite structures based on measurements damaged by ancillary data. In some examples, the measurement model is trained on dense target results that match the measurements in the cell to filter out the effects of collateral gratings.

For systems where a larger illumination area is limited by light, which means faster measurements (e.g., SAXS), this also creates recipes based on measurements from simple targets (e.g., from separate signals). This means that it can be trained to report these measurements based on complex on-device measurements.

In another additional aspect, measurement decomposition is utilized to optimize the measurement recipe for a particular measurement application. Optimized measurement recipes include the selection of physical parameters of the measurement system that enhance the signal of interest and suppress the signal from the ancillary structures.

As depicted in FIGS. 9A and 9B and FIGS. 10A and 10B, the observability of scattering from the underlying structure in the detector depends on the selection of the illumination angle of incidence. In measurement applications focusing on the final patterned structure, proper selection of the illumination angle of incidence enables suppression of incidental scattering signals originating from the underlying structure. In general, system modeling based on measurement decomposition enables simulation of various measurement recipes (i.e., a combination of measurement system parameter values) that enhance signals of interest and suppress signals from ancillary structures. Exemplary system parameters include, but are not limited to, divergent shape, illumination spot shape, illumination spot location, angle of incidence, azimuth, exposure time, target orientation, and source shape. By using a sampling strategy for decomposition measurements as described herein, the degree of signal contamination can be quantified by raw signal residual or measurement results through simulation of the decomposition model. In addition, these results may be verified by actual measurement when a feasible target exists.

In another aspect, process correction is determined based on measured values of the parameters of interest (eg, critical dimensions, overlay, height, sidewall angle, etc.), and corrections are made to process tools (eg, , Lithography tool, etching tool, deposition tool, etc.) to change one or more process control parameters of the process tool. In some embodiments, SAXS measurements are performed and process control parameters are updated while the process is running on the measured structure. In some embodiments, SAXS measurements are performed after a particular process step, and the process control parameters associated with the process step are updated for processing of future devices by the process step. In some embodiments, SAXS measurements are performed after a specific process step, and the process control parameters associated with the subsequent process step are updated for processing of the measured device or other device by the subsequent process step.

In some examples, the value of the measured parameter determined based on the measurement method described herein can be passed to an etching tool to adjust the etching time to achieve the desired etching depth. In a similar manner, etch parameters (eg, etch time, diffusivity, etc.) or deposition parameters (eg, time, concentration, etc.) are added to the measurement model to provide active feedback to the etch tool or deposition tool, respectively. It may be included. In some examples, modifications to process parameters determined based on measured device parameter values may be communicated to the process tool. In one embodiment, computing system 130 determines the value of one or more noted parameters during the process based on the measured signal 135 received from metrology system 100. In addition, the computing system 130 passes control commands 138 to process tools (eg, etch tools, ion implantation tools, lithography tools, etc.) based on the determined values of one or more noted parameters. The control command 138 causes the process controller to change the state of the process (e.g., stop the etching process, change the diffusivity, change the lithographic focus, change the lithography dose, etc.). In one example, control command 138 causes the process controller to stop the etch process when the desired etch depth is measured. In another example, control command 138 causes the process controller to change the etch rate to improve the measured wafer uniformity of the CD parameter.

In general, as incident x-ray illumination interacts with periodic features, x-ray illumination scatters coherently, partially coherently or non-coherently, thereby diffraction images on detector 119 (eg, The images 185 and 186 depicted in FIGS. 9B and 10B are generated. The desired scattered image or sequence of scattered images is achieved if the process tool is properly adjusted. However, when the measured image deviates from the desired image or the desired sequence of images, these deviations represent a process tool drift and also a correction to the process control parameters needed to bring the process tool back to the proper tune.

In general, a metrology target is characterized by an aspect ratio defined as a maximum height dimension (i.e., dimension perpendicular to the wafer surface) divided by the maximum transverse extent dimension of the metrology target (i.e., dimension aligned with the wafer surface). Depicted. In some embodiments, the metrology target to be measured has an aspect ratio of at least 20. In some embodiments, the metrology target has an aspect ratio of at least 40.

11A-11C depict isometric, top, and cross-sectional views, respectively, of a typical 3D FLASH memory device 195 that is to be measured in the manner described herein. The overall height (or equivalently depth) of the memory device 195 ranges from 1 micrometer to several micrometers. The memory device 195 is a vertically manufactured device. Vertically fabricated devices, such as memory device 195, essentially rotate the conventional planar memory device 90 degrees to orient the bit lines and cell strings vertically (perpendicular to the wafer surface). To provide sufficient memory capacity, a large number of alternating layers of different materials are deposited on the wafer. This requires the patterning process to perform well up to a depth of a few microns for structures with a maximum transverse range of 100 nanometers or less. As a result, aspect ratios of 25 to 1 or 50 to 1 are not uncommon.

Although FIG. 1 depicts a transmission SAXS measurement system, in order to measure shallow features in the manner described herein, generally, a reflective SAXS (R-SAXS) measurement system may be utilized.

12 depicts an exemplary R-SAXS metrology system 200 for measuring wafer 201 based on x-ray scatterometry measurements of semiconductor structures disposed on the wafer. The R-SAXS measurement system 200 includes a reflective x-ray scatterometer. Wafer 201 is attached to a wafer chuck 205 and is placed relative to the x-ray scatterometer by wafer stage 240.

In the depicted embodiment, the R-SAXS metrology system 200 is configured to generate x-ray illumination sources 210 suitable for reflective SAXS measurements similar to the description of the illumination source 110 with reference to FIG. 1. It includes.

In some examples, computing system 130 delivers command signal 237 to x-ray illumination source 210 to cause x-ray illumination source 210 to emit x-ray radiation at a desired energy level. The energy level is altered to obtain measurement data with a lot of information about the high aspect ratio structure being measured.

The illumination beam 216 illuminates the sample 201 over the measurement spot 202. After incident on the wafer 201, the scattered x-ray radiation 214 is collected by the X-ray detector 219 and the properties of the sample 201 sensitive to incident x-ray radiation according to the reflective SAXS measurement modality To generate an output signal 235. In some embodiments, scattered x-rays 214 are collected by x-ray detector 219, while sample positioning system 240 is transferred from computing system 230 to sample positioning system 240 The sample 201 is positioned and oriented to produce a scattered x-ray that is decomposed angularly according to the command signal 239.

In a further aspect, computing system 230 is utilized to determine the properties (eg, structural parameter values) of wafer 201 based on one or more diffraction orders of scattered light. As depicted in FIG. 13, the system 200 acquires the signal 235 generated by the detector 219 and determines the properties of the sample based at least in part on the obtained signal and of the determined value of the parameter of interest. And a computing system 230 utilized to store the indication 222 in a memory (eg, memory 290).

Generally, computing system 130 is configured to access model parameters in real time utilizing Real Time Critical Dimensioning (RTCD), or it is at least one sample parameter value associated with sample 101 You can also access a library of precomputed models to determine the value of. Generally, some form of CD engine may be used to evaluate the difference between an assigned CD parameter of a sample and a CD parameter associated with a measured sample. Exemplary methods and systems for calculating sample parameter values are described in US Pat. No. 7,826,071 issued November 2, 2010 to KLA-Tencor Corp., the entire contents of which are incorporated herein by reference. do.

In another aspect, one or more SAXS systems are configured to measure a number of different areas of the wafer during a process interval. In some embodiments, the wafer uniformity value associated with each measured parameter of interest is determined based on the measured value of each parameter of interest across the wafer.

In some embodiments, multiple metrology systems are integrated with process tools and the metrology system is configured to simultaneously measure different areas across the wafer during the process. In some embodiments, a single metrology system integrated with process tools is configured to sequentially measure multiple different areas of the wafer during the process.

In some embodiments, methods and systems for SAXS based metrology of semiconductor devices as described herein apply to measurement of memory structures. These embodiments enable critical dimension (CD), film and composition measurements for periodic and planar structures.

Scatterometry measurements, as described herein, may be used to determine properties of various semiconductor structures. Exemplary structures include low-dimensional structures such as FinFET, nanowire or graphene, structures less than 10 nm, lithographic structures, through substrate via (TSV), memory structures such as DRAM, DRAM 4F2, FLASH, MRAM, and high aspect ratio memory structures. Exemplary structural properties include geometric shape parameters such as line edge roughness, line width roughness, pore size, pore density, sidewall angle, profile, critical dimension, pitch, thickness, overlay, and material parameters such as electron density, composition, particle structure ( grain structure, morphology, stress, strain, and elemental identification, but are not limited to these. In some embodiments, the metrology target is a periodic structure. In some other embodiments, the metrology target is aperiodic.

In some examples, spin transfer torque random access memory (STT-RAM), three dimensional NAND memory (3D-NAND) or vertical NAND memory (V-NAND) , Dynamic random access memory (DRAM), three dimensional FLASH memory (3D-FLASH), resistive random access memory (Re-RAM), and phase change random access memory (DRAM) Measurement of critical dimensions, thickness, overlay, and material properties of high aspect ratio semiconductor structures, including but not limited to phase change random access memory (PC-RAM), is a T-SAXS measurement as described herein. This is done using the system.

In some examples, the measurement model is implemented as an element of the SpectraShape® critical dimensioning system available from KLA-Tencor Corporation of Milpitas, California, USA. In this way, a model is created and ready for use immediately after the scattering image is collected by the system.

In some other examples, the measurement model is implemented offline by, for example, a computing system implementing AcuShape® software available from KLA-Tencor Corporation of Milpitas, California, USA. The resulting model may be integrated as an element of the AcuShape® library accessible by metrology systems that perform measurements.

13 illustrates a method 300 for performing metrology measurements in at least one new aspect. Method 300 is suitable for implementation by a metrology system such as the SAXS metrology system illustrated in FIGS. 1 and 12 of the present invention. In one aspect, the data processing block of method 300 may be executed through pre-programmed algorithms executed by one or more processors of computing system 130, computing system 230, or any other general purpose computing system. It is recognized that there is. It is recognized herein that the specific structural aspects of the metrology system depicted in FIGS. 1 and 12 do not represent limitations and should be interpreted as illustrative only.

In block 301, an amount of x-ray illumination light is provided to one or more structures disposed on the semiconductor wafer in the measurement region.

In block 302, the amount of x-ray light reflected from or transmitted through the semiconductor wafer is detected in response to the amount of x-ray illumination light.

In block 303, a plurality of output signals are generated. The output signal represents the measured scattering response from one or more structures.

In block 304, one or more structures are decomposed into a plurality of sub-structures, a measurement region is decomposed into a plurality of sub-regions, or both.

In block 305, a structure model is generated that is associated with each of the plurality of sub-structures, each of the plurality of sub-structures, or both.

At block 306, a simulated scattering response associated with each of the structure models is generated independently.

At block 307, the simulated scattering response is combined to generate a combined simulated scattering response.

At block 308, the value of one or more of the parameter of interest associated with the one or more structures is determined based on the combined simulated scattering response and the measured scattering response.

In another embodiment, system 100 includes one or more computing systems 130 utilized to perform measurements of semiconductor structures based on scatterometry measurement data collected according to the methods described herein. One or more computing systems 130 may be communicatively coupled to one or more detectors, active optical elements, process controllers, and the like. In one aspect, one or more computing systems 130 are configured to receive measurement data associated with scatterometry measurements of the structure of wafer 101.

It should be appreciated that one or more steps described throughout this disclosure may be performed by a single computer system 130, or, alternatively, multiple computer systems 130. In addition, different subsystems of system 100 may include computer systems suitable for performing at least some of the steps described herein. Therefore, the above-mentioned description should not be interpreted as a limitation on the present invention, but merely as an example.

Further, the computer system 130 may be communicatively coupled to the spectrometer in any manner known in the art. For example, one or more computing systems 130 may be coupled to computing systems associated with a scattering system. In another example, the scatterometer may be controlled directly by a single computer system coupled to computer system 130.

The computer system 130 of the system 100 receives and / or receives data or information from a subsystem of the system (eg, scatterometer, and the like) by a transmission medium that may include a wired portion and / or a wireless portion. It may be configured to obtain. In this way, the transmission medium may serve as a data link between the computer system 130 and other subsystems of the system 100.

The computer system 130 of the system 100 receives data or information (eg, measurement results, modeling inputs, modeling results, etc.) from other systems by a transmission medium that may include a wired part and / or a wireless nothing. It may be configured to receive and / or acquire. In this manner, the transmission medium may serve as a data link between the computer system 130 and another system (eg, memory onboard metrology system 100, external memory, or other external system). For example, computing system 130 may be configured to receive measurement data from a storage medium (ie, memory 132 or external memory) via a data link. For example, the scattered image obtained using a scatterometer described herein may be stored in a permanent or semi-permanent memory device (eg, memory 132 or external memory). In this regard, scatterometry images may be imported (imported) from on-board memory or from an external memory system. Also, the computer system 130 may transmit data to another system through a transmission medium. For example, an estimated parameter value or measurement model determined by computer system 130 may be transferred and stored in external memory. In this regard, measurement results may be exported (exported) to other systems.

Computing system 130 may include, but is not limited to, a personal computer system, mainframe computer system, workstation, image computer, parallel processor, or any other device known in the art. In general, the term “computing system” may be broadly defined to encompass any device having one or more processors executing instructions from a memory medium.

Program instructions 134 implementing methods such as the methods described herein may be transmitted over a transmission medium, such as a wire, cable, or wireless transmission link. For example, as illustrated in FIG. 1, program instructions 134 stored in memory 132 are transmitted to processor 131 via bus 133. The program instructions 134 are stored on a computer-readable medium (eg, memory 132). Exemplary computer readable media include read only memory, random access memory, magnetic or optical disks, or magnetic tape. Computing systems 230 comprising elements 231-234 are similar to computing systems 130 including elements 131-134, respectively, as described herein.

As described herein, the term “critical dimension” means any critical dimension of a structure (eg, lower critical dimension, intermediate critical dimension, upper critical dimension, sidewall angle, lattice height, etc.), any two or more Critical dimensions between structures (eg, distance between two structures), and displacement between two or more structures (eg, overlay displacement between overlay grid structures, etc.). Structures may include three-dimensional structures, patterned structures, overlay structures, and the like.

As described herein, the term “critical dimension application” or “critical dimension measurement application” includes any critical dimension measurement.

As described herein, the term “measurement system” is used to at least partially describe the properties of a sample in any aspect, including measurement applications such as critical dimension measurement, overlay measurement, focus / dose measurement, and composition measurement. Any system utilized. However, such terms in the technical field do not limit the scope of the term “measurement system” as described herein. In addition, the metrology system may be configured for measurement of patterned and / or unpatterned wafers. The metrology system can be configured from LED inspection tools, edge inspection tools, backside inspection tools, macro inspection tools, or multi-mode inspection tools (concurrently involving data from one or more platforms), and calibration of system parameters based on critical dimension data. It can also be configured as any other metrology or inspection tool that benefits.

Various embodiments are described herein for a semiconductor measurement system that may be used to measure a sample in any semiconductor processing tool (eg, inspection system or lithography system). The term “sample” is used herein to refer to a wafer, reticle, or any other sample that may be processed (eg, inspected or printed for defects) by means known in the art. Is used.

As used herein, the term “wafer” generally refers to a substrate formed of a semiconductor or non-semiconductor material. Examples include, but are not limited to, monocrystalline silicon, gallium arsenide, and indium phosphide. Such substrates may be commonly found and / or processed in semiconductor manufacturing facilities. In some cases, a wafer may include only a substrate (ie, a bare wafer). Alternatively, the wafer may include one or more layers of different materials formed on the substrate. One or more layers formed on the wafer may be "patterned" or "not patterned". For example, a wafer may include multiple dies with repeatable pattern features.

"Reticle" may be a reticle at any stage of the reticle manufacturing process, or it may be a completed reticle that may or may not be released for use in a semiconductor manufacturing facility. A reticle, or "mask," is generally defined as a substantially transparent substrate in which a substantially opaque region is formed and composed of a pattern. The substrate may include, for example, a glass material such as amorphous SiO ₂ . The reticle may be placed over a wafer coated with resist during the exposure step of the lithography process, such that the pattern on the reticle may be transferred to the resist.

One or more layers formed on the wafer may or may not be patterned. For example, a wafer may include a plurality of dies each having repeatable pattern features. The formation and processing of such a layer of material may ultimately result in a finished device. Many different types of devices may be formed on a wafer, and the term wafer as used herein is intended to encompass wafers on which any type of device known in the art is fabricated.

In one or more exemplary embodiments, the functionality described may be implemented in hardware, software, firmware, or any combination thereof. When implemented in software, the functionality may be stored on a computer readable medium as one or more instructions or codes, or may be transmitted over a computer readable medium. Computer-readable media includes both computer storage media and communication media including any medium that facilitates transfer of a computer program from one place to another. The storage medium may be any available medium that can be accessed by a general purpose computer or special purpose computer. By way of example, and not limitation, such computer-readable media can be used as RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage device, or program code means desired in the form of instructions or data structures. It can be used for carrying or storing and can include a general purpose computer or special purpose computer, or any other medium that can be accessed by a general purpose processor or special purpose processor. Also, any connection is properly termed a computer-readable medium. For example, the software may use a coaxial cable, fiber optic cable, twisted pair, digital subscriber line (DSL), or wireless technology such as infrared, wireless, and microwave for a website, server, or other When transmitted from a remote source, coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technologies such as infrared, wireless, and microwave are included in the definition of the medium. Disks and discs, as used herein, include compact discs (CDs), laser discs, optical discs, digital versatile discs (DVDs), floppy discs, and Blu-ray discs. In this case, the disk (disk) generally reproduces data magnetically, and the disk (disc) optically reproduces data using a laser. Combinations of the above should also be included within the scope of computer readable media.

Although any particular embodiment has been described above for teaching purposes, the teaching of this patent document has general applicability and is not limited to the specific embodiments described above. Thus, various modifications, adaptations, and combinations of various features of the described embodiments can be practiced without departing from the scope of the invention as described in the claims.

Claims (20)

As an x-ray scatterometry based metrology system,
An X-ray illumination source configured to provide an amount of x-ray illumination light directed to one or more structures disposed on the semiconductor wafer within the measurement region;
configured to detect the amount of x-ray light reflected from or transmitted through the semiconductor wafer in response to the amount of x-ray illumination light and to generate a plurality of output signals representing measured scattering responses from the one or more structures Detector; And
Includes a computing system,
The computing system is:
To decompose the one or more structures into a plurality of sub-structures, to decompose the measurement region into a plurality of sub-areas, or to do both;
To generate a structural model associated with each of the plurality of sub-structures, each of the plurality of sub-regions, or both;
To independently generate a simulated scattering response associated with each of the structure models;
Combine each of the simulated scattering responses to produce a combined simulated scattering response; And
Based on the combined simulated scattering response and the measured scattering response to determine a value of one or more parameters of interest associated with the one or more structures
Constructed, x-ray scattering based measurement system. According to claim 1,
The plurality of sub-structures, which will include different periodic shapes of the same periodicity, x-ray scatterometry-based measurement system. According to claim 1,
The plurality of sub-structures, which comprises different periodic shapes with different periodicity, x-ray scatterometry-based measurement system. According to claim 1,
The plurality of sub-structures, which includes a shape that is repeated a plurality of times in an almost periodic manner, an x-ray scattering measurement based measurement system. According to claim 1,
The plurality of sub-structures include a first structure having a relatively small period and a second structure having a relatively large period that is an integer multiple of the small period, an x-ray scatterometry-based measurement system. According to claim 1,
Each of the plurality of sub-regions is associated with decomposition into a single structure or a plurality of sub-structures of the single structure, an x-ray scatterometry-based metrology system. According to claim 1,
Each contribution of the plurality of sub-regions to the intensity of the combined simulated scattering response at the detector is scaled in proportion to the area of each sub-region, based on x-ray scatterometry. Instrumentation system. According to claim 1,
The generation of the simulated scattering response associated with each of the structure models involves the calculation of scattered fields associated with each structure model using an electromagnetic modeling solver. Scattering-based measurement system. The method of claim 8,
Generation of the simulated scattering response associated with each of the structure models involves propagating the scattering field through a system model to reach the simulated scattering response associated with each structure model at the detector. , X-ray scattering based measurement system. According to claim 1,
The generation of the combined simulated scattering response involves combining each of the simulated scattering responses coherently, incoherently, or a combination thereof, Measurement system based on x-ray scatterometry. According to claim 1,
The computing system also:
An x-ray scatterometry based metrology system configured to deliver an indication of the value of the one or more noted parameters to the manufacturing tool that causes a manufacturing tool to adjust the value of one or more process control parameters of the manufacturing tool. According to claim 1,
The positive x-ray illumination light is directed to a measurement spot at a plurality of incidence angles, azimuth angles, or both, an x-ray scatterometry-based measurement system. According to claim 1,
The x-ray illumination source is also configured to provide the amount of x-ray illumination light directed to a measurement spot at a plurality of different energy levels, an x-ray scatterometry based metrology system. According to claim 1,
Determining the value of the one or more noted parameters is based on a model-based measurement model, a trained signal response metrology (SRM) measurement model, or a tomographic measurement model, Measurement system based on x-ray scatterometry. According to claim 1,
The at least one structure comprises a three-dimensional NAND structure or a dynamic random access memory (DRAM) structure, an x-ray scatterometry-based measurement system. According to claim 1,
The one or more structures include at least one construct of interest and at least one incidental structure, and the combined simulated scattering response is modeled from the at least one construct of interest and the at least one collateral structure An x-ray scatterometry based metrology system, which includes the contributions made. According to claim 1,
The one or more structures include at least one noted structure and at least one ancillary structure, and the computing system also includes:
Configured to filter the measured scattering response to reduce contribution from the at least one ancillary structure, wherein the determination of the value of the one or more notable parameters associated with the at least one notable structure is the filtered measurement. An x-ray scatterometry based metrology system based on the scattered response. As a measurement system based on x-ray scattering method,
An x-ray illumination source configured to provide a certain amount of x-ray illumination light directed to one or more structures disposed on the semiconductor wafer within the measurement area;
configured to detect the amount of x-ray light reflected from or transmitted through the semiconductor wafer in response to the amount of x-ray illumination light and to generate a plurality of output signals representing measured scattering responses from the one or more structures Detector; And
A non-transitory computer-readable medium comprising instructions, which instructions, when executed by one or more processors, cause the one or more processors to:
Decomposing the one or more structures into a plurality of sub-structures, decomposing the measurement region into a plurality of sub-regions, or both;
Generate a structure model associated with each of the plurality of sub-structures, each or both of the plurality of sub-structures;
Independently generate a simulated scattering response associated with each of the structure models;
Combining each of the simulated scattering responses to produce a combined simulated scattering response; And
An x-ray scatterometry-based metrology system that allows determining the value of one or more parameters of interest associated with the one or more structures based on the combined simulated scattering response and the measured scattering response. As a method,
Providing a predetermined amount of x-ray illumination light directed to one or more structures disposed on the semiconductor wafer within the measurement area;
detecting an amount of x-ray light reflected from or transmitted through the semiconductor wafer in response to the amount of x-ray illumination light;
Generating a plurality of output signals representing measured scattering responses from the one or more structures;
Decomposing the one or more structures into a plurality of sub-structures, decomposing the measurement region into a plurality of sub-structures, or both;
Generating a structure model associated with each of the plurality of sub-structures, each of the plurality of sub-regions, or both;
Independently generating a simulated scattering response associated with each of the structure models;
Combining each of the simulated scattering responses to generate a combined simulated scattering response; And
And determining a value of one or more noted parameters associated with the one or more structures based on the combined simulated scattering response and the measured scattering response. The method of claim 19,
And passing an indication of the value of the one or more noted parameters to the manufacturing tool that causes a manufacturing tool to adjust the value of one or more process control parameters of the manufacturing tool.

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